Property
Machine Learning - Predicting Singapore's Housing Resale Prices
Model Accuracy: 97%
Tags: #Property #Regression #Forecasting #Singapore #DataVisualisation
Background
Singaporean government through the SIT channel subsidised the housing and development in 1930. Similar to British housing project, public housing was constructed as quickly and cheaply as possible at high densities and used for resettlement schemes, consisting of small units with basic amenities (source). In Singapore today, public housing is located within new towns, in communities that are intended to be self-contained, with services in proximity to housing blocks, and is either owned by or rented to residents.
From the business or investor perspective, transaction of HDB flat may be affected by the inflation and interest. Those elements may consume a large portion of the ROI and overall market return.
Furthermore, the HDB resale prices rose at faster pace than before hence time is of essence and a highly accurate model is needed to predict the resale price.
Solution
Analytico Asia setting up the machine learning model to make a prediction based on historical resale price from 26 different towns in Singapore. From 89,465 observations, the result generated a prediction with 97,2% of accuracy while considering the growth rate of the property annually. Here in Analytico Asia, we help you to make an informed and data-driven decision before investing and selling any HDB properties.